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Robust normal vector estimation in 3D point clouds through iterative principal component analysis

Abstract : This paper introduces a robust normal vector estimator for point cloud data. It can handle sharp features as well as smooth areas. Our method is based on the inclusion of a robust estimator into a Principal Component Analysis in the neighborhood of the studied point which can detect and reject outliers automatically during the estimation. A projection process ensures robustness against noise. Two automatic initializations are computed leading to independent optimizations making the algorithm robust to neighborhood anisotropy around sharp features. An evaluation has been carried out in which the algorithm is compared to state-of-the-art methods. The results show that it is more robust against a low and/or a non-uniform sampling, a high noise level and outliers. Moreover, our algorithm is fast relatively to existing methods handling sharp features. The code and data sets will be available online.
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Soumis le : lundi 23 mars 2020 - 09:02:41
Dernière modification le : samedi 11 avril 2020 - 17:37:24

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Julia Sanchez, Florence Denis, David Coeurjolly, Florent Dupont, Laurent Trassoudaine, et al.. Robust normal vector estimation in 3D point clouds through iterative principal component analysis. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2020, 163, pp.18-35. ⟨10.1016/j.isprsjprs.2020.02.018⟩. ⟨hal-02514851⟩

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